Search Results for author: Olaf Wiest

Found 8 papers, 5 papers with code

MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension

no code implementations10 Jun 2024 Khiem Le, Zhichun Guo, Kaiwen Dong, Xiaobao Huang, Bozhao Nan, Roshni Iyer, Xiangliang Zhang, Olaf Wiest, Wei Wang, Nitesh V. Chawla

Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding.

Natural Language Understanding Retrosynthesis

Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective

no code implementations6 Feb 2024 Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, Nitesh V. Chawla

The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction.

Prediction

Large Language Model based Multi-Agents: A Survey of Progress and Challenges

1 code implementation21 Jan 2024 Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges.

Decision Making Language Modeling +2

What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks

1 code implementation NeurIPS 2023 Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

In this paper, rather than pursuing state-of-the-art performance, we aim to evaluate capabilities of LLMs in a wide range of tasks across the chemistry domain.

In-Context Learning

Few-Shot Graph Learning for Molecular Property Prediction

1 code implementation16 Feb 2021 Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla

The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery.

Attribute Drug Discovery +9

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